designing electric vehicles charging...

DESIGNING ELECTRIC VEHICLES CHARGING NETWORKS

NETWORK DESIGN CHALLENGESLISBON CASE STUDY

ANGRA DO HEROÍSMO CASE STUDYDESIGNING THE NETWORK FOR AZORES

Carlos Santos Silva

MIT-Portugal / Instituto Superior Técnico

EV charging networks highlights

• Charging Points (CP)

– Slow charge (230-380 V /16-32A / 3.6-7.2 kVA – 8 hours)

– Fast charge ( 500V DC / 200 A – 20 minutes)

• Europe

– London, Oslo, Paris, Helsinki, Frankfurt, Lisbon, Amsterdam

– EV charging network is designed for:

• Daylight parking (in the evening cars will charge at “home”)

• 5-10% in street parking / 90-95% parking garages

• Autonomy anxiety control

EV network design challenges

• Most charging will be made at home

– Upgrading infrastructure / power reinforcement

• Public network

– Anxiety control

• Visible places (main streets, shopping malls)

– Long distance drivers (taxi drivers, tourists)

• Public parking places

• Touristic places

– Integrated service with parking

– Commuters recharge (S. Miguel)

• Work place

• Fast charging It is all about where people park their car

- Detailed mobility patterns studies

Early Adopters

• European studies

– Man under 35 or above 55

– High, middle high class

– Higher education instruction

– Two cars

MOBI.E Highlights

• MOBI.E

– 1100 CP in 25 municipalities

• 1 master+ n slaves up to 2011

– street parking

– public parking garages

• Municipalities choose CP location

– Public space electric parking

• have to be connected to MOBI.E

MOBI.E Charging Points

MOBI.E framework

Methodology to design EV charging network

1. Estimate EV penetration in the vehicle fleet

– Using MOBI.E study from Roland Berger

– Consider a service rate to define number of required CP

2. Characterize region/city/neighborhood

3. Define Criteria for location of CP

4. Use a decision-making algorithm

– Multi-Atribute (PROMETHEE)

Methodology Implementation

Decision-aid tool

For each year (2010 - …):

1. Evaluate alternatives2. Compare alternatives3. Alocate CP to alternatives

EV projections for region/city/neighborhood

Parking characterization ofRegion/city/neighborhood

(alternatives)

Location Criteria(Decision makers)

Final CP allocation (alternatives)

LISBON

1 - EV Projections assumptions

1 – Service Degree

2 - Characterization of the City

2

3

7

4

5

6

14

13

12

1110

981

16

15

18

17

2019

22

21

24

23

25

2827

30

2931

26

34

33

363537

3239

38

40

3 - Criteria

4 – Decision Making algorithm (PROMETHEE)

1. Initialization

– Alternatives

– Criteria

– Relative weight of criteria

– Preference function

• Maximize / Minimize

• Shape

𝐴 = 𝑎1 , 𝑎2 , 𝑎3, … , 𝑎𝑛

𝐺 = 𝑔1 𝑎𝑖 , 𝑔2 𝑎𝑖 , … , 𝑔𝑘 𝑎𝑖 𝑎𝑖 ∈ 𝐴

𝑔1 𝑎 𝑔2 𝑎 … 𝑔𝑘 𝑎

𝑤1 𝑤2 … 𝑤𝑘

𝑤𝑗 = 1

𝑘

𝑗=1

d

P

1

p

4 - Decision Making algorithm (PROMETHEE)

Parking offer

on the public

thoroughfare

Parking

offer in

parks of

public

access

Deterrent

parking

Parking

demand

pressure

Occupation

ratio

during the

day

Occupation

ratio

during the

night

Percentage

of still

vehicles

[%]

Parking

balance

for

residents

Unmet

parking

demand

for

residents

[%]

Number

charging

points

g 1 g 2 g 3 g 4 g 5 g 6 g 7 g 8 g 9 g d

10 10 10 15 5 10 5 5 5 25

Maximize Maximize Maximize Maximize Maximize Maximize Minimize Minimize Minimize Minimize

V-shape V-shape V-shape Level V-shape V-shape V-shape V-shape V-shape V-shape

p=14367 p=7357 p=1950 p=q=0,5 p=1,31 p=1,56 p=68 p=9100 p=55 p d

1 Carnide Norte 2934 1000 1000 0,5 0,67 0,95 57 1700 0 0

2 Lumiar Norte 813 1581 0 0 0,97 1,49 33 400 0 0

3 Charneca 3337 0 0 1 1,11 1,33 45 200 0 0

4 Benfica 9275 514 0 1 1,17 1,14 46 -800 5 0

5 Carnide sul 3746 7357 415 0,5 1,33 1,36 23 -600 10 0

6 Lumiar sul 7299 2200 1950 0 0,98 1,18 31 4600 0 0

7 Aeroporto 3 600 0 0 0,92 1,08 0 0 0 0

8 Olivais 7390 2193 0 1 1,11 1,17 14 -1800 16 0

9 Oriente 7155 6715 0 0 0,93 0,44 30 4100 0 0

10 Monsanto 164 0 0 0,5 1,1 1,32 0 -600 55 0

… … … … … … … … … … … …

Criteria

Weight [%]

Objective

Preference function


2. Evaluation

– Difference table

– Aggregation

– Outranking flows

– Preference

𝑑𝑗 𝑎𝑖 , 𝑎𝑙 = 𝑔𝑗 𝑎𝑖 − 𝑔𝑗 𝑎𝑙

𝜋 𝑎𝑖 , 𝑎𝑙 = 𝑃𝑗 (𝑎𝑖 , 𝑎𝑙)𝑤𝑗

𝑘

𝑗=1

𝑎1 𝑎2 𝑎3 … 𝑎𝑛

𝑎1 0 𝜋 𝑎1, 𝑎2 𝜋 𝑎1, 𝑎3 … 𝜋 𝑎1, 𝑎𝑛 𝜙+ 𝑎1 𝑎2 𝜋 𝑎2, 𝑎1 0 𝜋 𝑎2, 𝑎3 … 𝜋 𝑎2, 𝑎𝑛 𝜙+ 𝑎2 𝑎3 𝜋 𝑎3, 𝑎1 𝜋 𝑎3, 𝑎2 0 … 𝜋 𝑎3, 𝑎𝑛 𝜙+ 𝑎3 … … … … … … … 𝑎𝑛 𝜋 𝑎𝑛 , 𝑎1 𝜋 𝑎𝑛 , 𝑎2 𝜋 𝑎𝑛 , 𝑎3 … 0 𝜙+ 𝑎𝑛

𝜙− 𝑎1 𝜙− 𝑎2 𝜙− 𝑎3 … 𝜙− 𝑎𝑛

𝜙− 𝑎 =1

𝑛 − 1 𝜋(𝑥, 𝑎)

𝑥∈𝐴

𝜙+ 𝑎 =1

𝑛 − 1 𝜋(𝑎, 𝑥)

𝑥∈𝐴

𝑎𝑃𝐼𝐼𝑏 𝑖𝑓𝑓 𝜙 𝑎𝑖 > 𝜙 𝑎𝑙

𝑎𝐼𝐼𝐼𝑏 𝑖𝑓𝑓 𝜙 𝑎𝑖 = 𝜙 𝑎𝑙

4 – Example of application

CG – Campo Grande

AL – Alvalade

Parking offer on the

public thoroughfare

Percentage of still

vehicles

d

P

1

55 d(CG,AL) d(AL,CG)

0,29

0

d

P

1

14367d(AL,CG)d(CG,AL)

0,28

0

10% 5%

d(AL,CG)=4052 d(AL,CG)=16

d(CG,AL)=-4052 d(CG,AL)=-16

Example:

Alvalade Campo Grande>


Yes

No

Calculate

difference tables

Normalize data

with preference

functions

Preferences

aggregation

Calculate

outranking flows

Complete ranking

Constrains

verified

Update

alternatives

ranking

Yes

No

Update dynamic

evaluation criteria

Stopping criteria

verified

Calculate dynamic

criterion data

Calculate dynamic

difference tables

Normalize

dynamic data with

preference

function

No

Yes

Iterations

completed

Update stopping

criteria, dynamic

preference

function

parameters,

constrains and

iteration Impossible to

fulfill constrains

No

Yes

Evaluation

criteria

Preference

functions

parameters

Stopping

criteria

Criteria

weights

Dynamic

preference

function

parameters

Dynamic

evaluation

criteria

Coalescence

matrix

Iterations

Constrains

Start

Stop

Stop

List the

actions taken

in all

iterations

List all the

actions taken

until this

point

List the

actions taken

5 – Results (2011 -125CP)

0

7

0

9

10

6

0

1

4

40

2104

1

7

1

3

07

4

5

7

10

0

10

6

04

0

0

0

431

04

0

0

5 – Results (2015 -1009 CP)

12

52

5

58

65

41

11

14

33

148

226432

19

49

18

27

650

33

41

51

10

1

1410

45

732

14

8

10

322914

1231

8

7

ANGRA DO HEROISMO (TERCEIRA)

1 - EV Projections assumptions

• The ratio between Angra do Heroismo and Portuguese

fleet will remain constant (15.5 k / 4.5 m 0,34%)

• Same penetration rate as in Portugal

– Lisbon considered that at the beginning 50% of EV will be

located there and will decrease to the average

– In Azores, with GIP, it may be expected also higher rates

• The considered area (downtown represents 25% parking

places in Angra)

1 - EV Projections results

0

100

200

300

400

500

600

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Nú

me

ro d

e v

eíc

ulo

s e

léct

rico

s p

ara

An

gra

Nú

me

ro d

e V

eíc

ulo

s e

létr

ico

s p

ara

Po

rtu

gal

Electric Vehicles Projections for Portugal and Angra do Heroismo

Veículos elétricos em Portugal Veículos eléctricos em Angra

1 - Service Rate

• In 2010, 1 CP for each 2 EV (50% service rate)

• In 2020, 1 CP for each 5 EV (20% service rate)

0,0

0,1

0,2

0,3

0,4

0,5

0,6

20

10

20

11

20

12

20

13

20

14

20

15

Razão Postos Carregamento por VE

Razão Postos Carregamento por VE

2 – Characterization of city

2 – Number of public parking places

Lugares de estacionamento

Lugares em Parques

A 273 0

B 510 80

C 265 0

D 378 0

E 360 80

F 410 95

G 337 0

Parque de Santa Luzia 0 80

Parque do Bailão 0 389

Parque da Praça de Touros

0 340

3 – Criteria for Location

Location

Supply Demand Number of

existing CPOther

Angra do Heroísmo CP network - Criteria

Lugares de estacionamento

Lugares em Parques

Pontos de carregamento

Peso dos critérios 30% 20% 50%

A 273 0 0

B 510 80 0

C 265 0 0

D 378 0 0

E 360 80 0

F 410 95 0

G 337 0 0

Parque de Santa Luzia

0 80 0

Parque do Bailão 0 389 0

Parque da Praça de Touros

0 340 0

Angra do Heroísmo CP network - Results

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020A 0 0 0 0 0 0 0 0 1 1 1B 0 0 1 1 2 2 3 3 4 4 5C 0 0 0 0 0 0 0 0 0 0 0D 0 0 0 1 1 1 1 1 1 1 2E 0 0 1 1 1 1 2 2 2 3 3F 0 0 0 1 1 2 2 2 3 3 3G 0 0 0 0 0 0 0 1 1 1 1Santa luzia 0 0 0 0 0 1 1 1 1 1 1Bailão 1 1 1 1 2 3 3 4 5 5 6Praça de touros 0 1 1 1 2 2 3 3 4 4 5

Total 1 2 4 6 9 12 15 17 22 23 27

Angra do Heroísmo CP network – 2010 (1CP)

0

0

00

0

1

0

0

0

0


1

0

02

1

3

1

2

0

2


3

1

05

2

6

1

3

1

5

ELECTRIC MOBILITY IN AZORES

Why EVs in Azores?

• 33% of the primary energy is Azores is used on road

transportation

– 1,38 millions of barrels of oil

– 85 millions € [ 80$ per barrel, $=1,3€] + refinery and transportation costs

0

3000

6000

9000

12000

15000

18000

2001 2002 2003 2004 2005 2006 2007

TJ

Renewables

Kerosene

Butane

Gasoline

Diesel

Fuel Oil

0

3000

6000

9000

12000

15000

18000

2001 2002 2003 2004 2005 2006 2007

TJ

Residential

Public Services

Commerce

Industry

Agriculture

Electricity Production

Road Transportation

Electric Mobility Impact (SM)

• Significant reduction in fossil fuels consumption

– Mild electricity consumption increase (with energy efficiency)

– Has to be based on renewable electricity

Terceira as the “earlier adopter”

• Lower energy consumption for road transportation in Terceira in average

– 11% of SM

• Local agents (municipalities, consumers) want EVs!

The plan for Azores (public network)

• All islands

– All municipalities?

• Technical vs Economic vs Social

• Fast charging

– Eventually on SM

• Creating value

– Business models

• Car sharing for tourists

• Transportation (taxi, shuttles)

• Parking / energy management

– Reconversion

Designing Electric Mobility for Azores

Case Studies

• Charging network

• Fleet

• Production

• Fleet

• Production

• Distribution

FLEET ANALYSIS (FLORES)

Patrícia Baptista

Methodology

• Materials and WTT: GREET, Simapro

and other databases

• TTW: ADVISOR, Demb, Siemp, RVS

• Life-cycle analysis

Category 2000 2001 2002 2003 2004 2005 2006 2007 2008

Light duty vehicles -

Passengers 881 974 1,233 1,516 1,610 1,698 1,761 1,800 1,702


Goods 4 50 52 61 60 55 73 82 73


Commercial 48 309 332 298 306 323 305 296 244

Taxis 14 16 17 13 13 11 12 11 10

Rent-a-car vehicles 14 19 37 16 19 8 19 12 8

Other light duty

vehicles 10 11 12 10 10 11 11 12 12

Total LDV 971 1,379 1,683 1,914 2,018 2,106 2,181 2,213 2,049

Heavy duty vehicles

- Goods 40 40 45 37 37 39 42 41 44

Heavy duty vehicles

- Passengers 5 9 8 7 7 7 6 7 5

Other heavy duty

vehicles 7 8 8 7 7 7 8 9 10

Total HDV 52 57 61 51 51 53 56 57 59

Tractors for

agriculture 95 67 67 67 70 75 73 66 74

Tows for agriculture 0 3 3 1 1 1 0 0 0

Total agriculture 95 70 70 68 71 76 73 66 74

Mopeds 197 140 151 133 133 122 116 96 79

Motorcycles 22 30 40 54 61 67 74 85 83

Total 2W 219 170 191 187 194 189 190 181 162

Others 0 0 0 0 0 1 1 8 3

Tows 8 19 13 5 6 4 4 2 2

Machines (industrial

or others) 8 0 0 0 0 0 0 0 0

Total Flores 1,353 1,695 2,018 2,225 2,340 2,429 2,505 2,527 2,349

LDV Motorization index at 498 veh per

1000 inhab

• expected to stabilize at around

520 veh/ per 1000 inhab.

• HDV stabilizes at around 15 veh per

1000 inhab.

Flores Fleet historical data and evolution (ISP)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

20

09

20

11

20

13

20

15

20

17

20

19

20

21

20

23

20

25

20

27

20

29

20

31

20

33

20

35

20

37

20

39

20

41

20

43

20

45

20

47

20

49

New

ve

hic

le s

ale

s sh

are

s (%

)

unchanged market

EVs

PHEV diesel

PHEV gasoline

HEV diesel

HEV gasoline

Scenario – Electricity Powered (New Vehicles)

• Alternative fuels: 10% biodiesel in 2020 and 25% in 2050;

• In 2050 90% of vehicle sales will have been shifted from conventional technologies.

• An electricity recharging infrastructure will be available. Both PHEV and EV technologies start

entering the market right away.

• The assumed HEV:PHEV:EV ratio for 2050 was 30:50:20; and

• The diesel/gasoline share stabilizes at a 75/25 ratio.

-

200

400

600

800

1.000

1.200

1.400

1.600

1.800

Sc.1

Bas

elin

e tr

end

Sc. 2

Po

licy

ori

ente

d

Sc. 3

Ele

ctri

city

p

ow

ered

Nu

mb

er

of v

eh

icle

s

Gasoline LDV

LDV Diesel

HEV diesel

HEV gasoline

PHEV gasoline

PHEV diesel

EVs

0,00E+00

2,00E+07

4,00E+07

6,00E+07

8,00E+07

1,00E+08

1,20E+08

20

10

Po

licy

20

10

Ele

ctri

city

20

10

Bas

line

20

30

Po

licy

20

30

Ele

ctri

city

20

30

Bas

elin

e 2

05

0

Po

licy

20

50

Ele

ctri

city

20

50

TTW

En

erg

y C

on

sum

pti

on

pe

r te

chn

olo

gy (M

J)

EVs

PHEV diesel

PHEV gasoline

FCV HEV

FCV PHEV

HEV gasoline

HEV diesel

LDV Diesel

Gasoline LDV

Scenario – Electricity Powered (Energy Tank To Wheel)

0,00E+00

1,00E+07

2,00E+07

3,00E+07

4,00E+07

5,00E+07

6,00E+07

7,00E+07

8,00E+07

9,00E+07

20

09

20

12

20

15

20

18

20

21

20

24

20

27

20

30

20

33

20

36

20

39

20

42

20

45

20

48

LDV Electricity

LDV biodiesel

LDV diesel

LDV gasoline

ELECTRICITY PRODUCTION (FLORES)

André Pina

Flores electricity production system

• The electricity system is a combination of Wind-Hydro-Diesel.

• To maintain frequency and voltage stability at the grid level, a flywheel energy storage system is in place

– The system is able to achieve 100% renewable electricity

– In 2009, this happened for several hours of the day in at least 12 days of the year.

• Future investments:– Hydro, 1600 kW, 2011.

– Hydro, 1040 kW, 2012.

Flores electricity production historical data

Population of ~4200 habitants

Area of ~141 km2

54% of renewable electricity in 2009

Electricity production in Flores, 1994-2009

Approach taken

• Main uncertainties studied:

– Growth rate for electricity demand

– Fuel prices (2008 prices much higher than 2007 or 2009)

– EV penetration in fleet

Renewable energy penetration in electricity

generation

76%

78%

80%

82%

84%

86%

88%

90%

92%

94%

96%

2015 2020 2025 2030 2035 2040

Electricity generated from renewables

Scenario 1

Scenario 2

Scenario 3

Scenario 4

Scenario 5

Scenario 6

BASE

0

2

4

6

8

10

12

14

16

18

Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6 BASE

GW

h

Energy mix in 2035

Diesel

Wind

Hydro

GRID MANAGEMENT (FLORES)

Joel Soares

Network Topology

Ribeira Grande PowerPlant

Load diagrams

Scenarios characterization

• 50% EV penetration 1000 EV:

- 20% PHEV 1.5 kW rated power / 70% EV1 3 kW rated power / 10% EV2 6 kW

rated power.

• 550 EV charging simultaneously at a charging rate of 0.8 C 1321 kW + 800 kW of

conventional load = total load of 2121 KW

Three scenarios were studied considering 100% renewable electricity:

- 0% of adherents to the smart charging scheme (0 EV) it was impossible to manage

EV load for primary frequency control;

- 25% of adherents to the smart charging scheme (250 EV) it was possible to manage

EV load between 1108 – 1392 kW for primary frequency control;

- 100% of adherents to the smart charging scheme (1000 EV) it was possible to

manage EV load between 344 – 1647 kW for primary frequency control..

• Frequency disturbance impact on the battery charging current

Converter Response

0 0.5 1 1 .5 2 2.549.2

49.4

49.6

49.8

50

50.2

Time [s]

Fre

qu

en

cy [

Hz]

Frequency Disturbance (load increase) [Hz]

0 0.5 1 1 .5 2 2.53

4

5

6

7

8

Time [s]

Batt

ery

Cu

rren

t [A

]

Battery Charging Current [A]

Battery Current

Battery Current Reference

Load Switch On at 0,4 s

Set-point

value 6 A

Dead-Band

Droop Control

0 0.5 1 1 .5-30

-20

-10

0

10

20

30

Time [s]

Ph

ase C

urr

en

ts [

A]

Phase currents during frequency disturbancy

•Wind speed decrease Shortfall of 450 kW in the wind power production

Dynamic Simulation Results

0

100

200

300

400

500

600

290 310 330 350 370 390

kW

ms

Scenario 3 :

•1000 EV;

• 550 EV charging simultaneously;

100% smart charging adherents

designing electric vehicles charging...

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